1. Field of the Invention
The present invention relates to a method for counting people passing through a gate, and more particularly, to a method for counting people passing through a gate by analyzing the area of the people, the colors of clothing the people wear, and the moving patterns of the people.
2. Description of the Prior Art
An accurate automatic counting of pedestrian flow through a gate is very attractive for the entry control and access surveillance of important military, building security and commercial applications. Without losing the generality, the early automatic counting approaches, such as turn stiles, rotary bars, and light beams, had suffered one intractable problem: they could not count the passing people accurately unless there is only one pedestrian passing through the gate at one time. To solve this problem, many image-processing based approaches with various applications are hence motivated and all provide a real-time automatic counting for people passing through a specific region of interest by analyzing a series of images captured with a video camera.
For the transportation applications, Bartolini et al. and Albiol et al. addressed the problems of determining the number of people getting into and out of a bus and train, respectively. To avoid the occlusion problem, Rossi and Bozzoli and Sexton et al. mounted the camera vertically with respect to the floor plane and set the optical axis of the camera in such a way that the passing people could be observed from just overhead.
The system based on template motion-estimation tracking may be very time-consuming because the computation complexity increases substantially with the increasing number of pedestrians and may suffer from people-touching overlapping. Focused on dynamic backgrounds, Zhang and Sexton developed an automatic pedestrian counting method on an escalator or a moving walkway by using a model-specified directional filter to detect object candidate locations followed by a novel matching process to identify the pedestrian head positions in an image even with complicated contents. With the gray-level-based head analysis, the method will suffer from the following situations: a low contrast of the head image with the background and hairstyles or pedestrians wearing various hats. The first case illustrates that the gray-level technique cannot provide sufficient information for extracting the required pattern from an image, and the second case reveals that various sizes and shapes of the human body due to clothing may affect model-based processing.
To increase the count of people passing through a gate at one time, Terada et al. used the stereo images captured by a pair of cameras to cope with both problems of the crowd counting and direction recognition of the passing people. The setting of the stereo camera is complicated and the measurement will be seriously sensitive to any shift of camera. To avoid limiting the setting position of the camera and counting several times for a single person as they move around, multiple cameras located over the region of interest will be the allowable solution. Based on the cost-effective consideration, a single camera with a tracking algorithm may be the better solution and thus Masoud and Papanikolopoulos developed a rectangular model-based recognition of the pedestrian with a human motion analysis to achieve a reliable people count. By setting a fixed single camera hung from the ceiling of the gate, Kim et al. proposed a real-time scheme to detect and track the people moving in various directions with a bounding box enclosing each person. Also using a single zenithal camera, Bescos et al. introduced a DCT based segmentation, which can efficiently consider both lighting and texture information to cope with some problems, such as shadows, sudden changes in background illumination and sporadic camera motion due to vibration, in order to count people crossing an entrance to a big store.
On the other hand, by taking advantage of human motion analysis, many techniques of the human body tracking or pedestrian detection may be applied to the pedestrian counting in open spaces, in which the camera is usually set with a downward-sloped view to obtain a more sufficient surveillance range. Nevertheless, the tracking process is always very computational-intensive and such a camera setting will result in being intractable to segmenting or recognizing each person in a crowd of pedestrians owing to the overlapping problem.
Some of the above people-counting methods can solve the problem of real-time counting for the crowded pedestrians. However, those methods have not dealt with another frequently-happening overlapping problem, called “people-touching overlapping”, which is resulted from the situation when bodies of pedestrians touch each other, e.g. walking close together, walking hand in hand and putting one's hand on another's shoulder or waist, in spite of using a zenithal camera. Also, they have not mentioned how to deal with the merge-split case that people walk sometimes touching with one another and sometimes separating from others. To overcome the above problems, an area and color information based approach is proposed but some problems of tracking needs to be improved for increasing the counting accuracy, especially when the pedestrian walks fast.